Scholarlink

In order to evaluate kidney function non-invasively, this study presents an AI-powered optical sensor device. This method uses Convolutional Neural Networks (CNNs) to assess kidney function after collecting a large amount of spectral data from kidney tissues using near-infrared (NIR) spectroscopy. It takes a lot of time and effort to conduct traditional diagnostics like blood tests and biopsies. Employing this technology can allow for accurate diagnoses to be made in real time instead of during an examination. It was found in a study of 200 subjects that the method used correctly was correct in 92% of cases. The likelihood of accurately classifying individuals with severe renal impairment from those without it is 90%, whereas the likelihood of correctly identifying them is 94%. These results clearly show that an AI approach based on optical sensing technology can indeed change the conventional way of monitoring the kidney’s function. Instantaneous and correct evaluations can enhance and optimize the clinical outcomes.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-Driven Optical Sensing Techniques for Non-invasive Kidney Function Evaluation

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Anuj Kumar Singh,
  • Udit Singh,
  • Neeraj Pandey,
  • Satyam Pandey

摘要

In order to evaluate kidney function non-invasively, this study presents an AI-powered optical sensor device. This method uses Convolutional Neural Networks (CNNs) to assess kidney function after collecting a large amount of spectral data from kidney tissues using near-infrared (NIR) spectroscopy. It takes a lot of time and effort to conduct traditional diagnostics like blood tests and biopsies. Employing this technology can allow for accurate diagnoses to be made in real time instead of during an examination. It was found in a study of 200 subjects that the method used correctly was correct in 92% of cases. The likelihood of accurately classifying individuals with severe renal impairment from those without it is 90%, whereas the likelihood of correctly identifying them is 94%. These results clearly show that an AI approach based on optical sensing technology can indeed change the conventional way of monitoring the kidney’s function. Instantaneous and correct evaluations can enhance and optimize the clinical outcomes.